Research project

This repository has been archived on 2025-07-30. You can view files and clone it, but you cannot make any changes to it's state, such as pushing and creating new issues, pull requests or comments.

Kevin Alberts fb9ce46bd8 Implement types of auto-encoders and corruption, use log scale in loss graphs, lots of helper function hooks in training process to allow implementations ... - Encoders: sparse, denoising, contractive and variational - Noise: gaussian		2020-12-29 18:44:44 +01:00
datasets	Initial, very basic framework for running comparison tests	2020-11-24 17:19:46 +01:00
models	Implement types of auto-encoders and corruption, use log scale in loss graphs, lots of helper function hooks in training process to allow implementations	2020-12-29 18:44:44 +01:00
saved_models	Initial, very basic framework for running comparison tests	2020-11-24 17:19:46 +01:00
.gitignore	First basic auto-encoder and CIFAR-10 dataset implemented	2020-12-28 13:09:18 +01:00
config.example.py	Implement types of auto-encoders and corruption, use log scale in loss graphs, lots of helper function hooks in training process to allow implementations	2020-12-29 18:44:44 +01:00
logging.conf	Implement types of auto-encoders and corruption, use log scale in loss graphs, lots of helper function hooks in training process to allow implementations	2020-12-29 18:44:44 +01:00
main.py	Implement types of auto-encoders and corruption, use log scale in loss graphs, lots of helper function hooks in training process to allow implementations	2020-12-29 18:44:44 +01:00
requirements.txt	Implement types of auto-encoders and corruption, use log scale in loss graphs, lots of helper function hooks in training process to allow implementations	2020-12-29 18:44:44 +01:00
utils.py	Implement types of auto-encoders and corruption, use log scale in loss graphs, lots of helper function hooks in training process to allow implementations	2020-12-29 18:44:44 +01:00